Task-based biometric differentiator of stress levels to maximize productivity

ABSTRACT

An aspect includes receiving a request to assign a task to a user based on biometric data. Correlated data that correlates characteristics of tasks previously performed by the user with observed productivity and stress levels of the user while performing the tasks is accessed. An optimization model is applied to select a task that maximizes a predicted productivity of the user in performing the task while at the same time minimizes a predicted negative stress level of the user while performing the task. Input to the optimization model includes the correlated data and characteristics of a plurality of potential tasks. The selected task is assigned to the user. The correlated data for the user is updated based on the observed productivity of the user in performing the selected task, the observed stress level of the user while performing the selected tax, and at least one characteristic of the selected task.

DOMESTIC PRIORITY

This application is a continuation of U.S. patent application Ser. No.14/821,978, filed Aug. 10, 2015, the content of which is incorporated byreference herein in its entirety.

BACKGROUND

The present invention relates to biometric feedback, and morespecifically, to assigning tasks using a task-based biometricdifferentiator of stress levels to maximize productivity.

SUMMARY

According to embodiments of the present invention, a method, system, andcomputer program product are provided for assigning tasks based onbiometric data. A request to assign a new task to a user is received andcorrelated data that correlates characteristics of tasks previouslyperformed by the user with observed productivity and stress levels ofthe user while performing the tasks is accessed. Each observed stresslevel is either a negative stress level or a positive stress level. Anoptimization model is applied to select, from a plurality of potentialtasks, a task that maximizes a predicted productivity of the user inperforming the task while at the same time minimizes a predictednegative stress level of the user while performing the task. Input tothe optimization model includes the correlated data and characteristicsof the plurality of potential tasks. The selected task is assigned tothe user. An observed productivity of the user in performing theselected task and an observed stress level of the user while performingthe selected task are received. The observed stress level is obtainedfrom a biometric sensor. The correlated data for the user is updatedbased on the observed productivity of the user in performing theselected task, the observed stress level of the user while performingthe selected tax, and at least one characteristic of the selected task.

Additional features and advantages are realized through the techniquesof the present invention. Other embodiments and aspects of the inventionare described in detail herein and are considered a part of the claimedinvention. For a better understanding of the invention with theadvantages and the features, refer to the description and to thedrawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The subject matter which is regarded as the invention is particularlypointed out and distinctly claimed in the claims at the conclusion ofthe specification. The forgoing and other features, and advantages ofthe invention are apparent from the following detailed description takenin conjunction with the accompanying drawings in which:

FIG. 1 depicts a process flow for assigning tasks based on biometricdata in accordance with an embodiment;

FIG. 2 depicts a process flow for establishing a biometric baseline inaccordance with an embodiment;

FIG. 3 depicts a process flow for assigning a new task and trackingbiometric data of the user in accordance with an embodiment; and

FIG. 4 depicts a block diagram of a system for assigning tasks based onbiometric data in accordance with an embodiment.

DETAILED DESCRIPTION

Embodiments described herein are directed to increasing workerproductivity by using biometric data when assigning work items, ortasks. In accordance with embodiments, tasks are assigned based on thepremise that a “happy” employee is a “more productive” employee. When anemployee is working on an assigned task, biometric measurements of theemployee are recorded in an attempt to assess an amount of stress thatperforming the task causes the employee. Characteristics of the task arecorrelated with the biometric measurement(s), and stored for use infuture task assignments to the employee. Embodiments attempt to reducethe employee's stress level by assigning tasks that have characteristicsin common with tasks previously performed by the employee that havecaused the employee the least amount of negative stress.

An example scenario where an embodiment may be utilized follows. Anemployee, “Rob”, is a member of a software team that is managed byanother employee, “John.” Rob has been getting tasks assigned to himthat he doesn't like, while there are other tasks that need to be donethat he would prefer. Since Rob doesn't like the tasks that he isassigned and they induce stress, his productivity level is beingnegatively affected. For example, he is taking longer than expected tocompete tasks, which directly affects the efficiency of the softwareteam. Rob does not want to report to John that he doesn't like the tasksthat are being assigned because he doesn't want his boss to think thathe can't perform the job. Rob believes that he would be able to performtasks at a higher rate of productivity if he enjoyed the tasks that Johnassigned him. Being the manager of the software team, John believes thathe is assigning tasks fairly because he doesn't receive any complaints.Embodiments described herein can give John a way to receive voluntaryfeedback from his team members based on their biometric reactions to theassigned tasks, and using this information John can assign tasks to histeam more strategically and increase the overall productivity of theteam.

Embodiments include an “opt-in” system which allows each user to decidewhether or not to allow biometric measurements to be recorded and usedby the system for use in task assignment to the user. When a useropt-out of having biometric measurements being recorded, the system canuse a default value for the biometric measurement.

Referring now to FIG. 1, a process flow 100 for assigning tasks based onbiometric data is generally shown in accordance with an embodiment. Inan embodiment, the processing shown in FIG. 1 is performed by taskmanagement application executing on one or more processors. At block102, baseline biometric data is collected for a user, the collectingincluding recording the user's level of stress at various points in theday while performing known tasks. At the same time, information aboutthe tasks that the user is performing while the baseline biometric datais being collected is recorded. The information can include, but is notlimited to characteristics of the tasks and productivity of the user inperforming those tasks. The data collection at block 102 can beperformed for a specified time period such as four to six weeks toestablish a baseline. See FIG. 2 and the accompanying description belowfor a more detailed description of an embodiment of the processperformed at block 102 for establishing a baseline.

At block 104, the biometric data is correlated to the tasks that wereperformed. In an embodiment, the correlation is performed using astatistical data analysis package, or program, that can be used tocorrelate the productivity in a category of work to the levels of stressdetected while dealing with those categories. The category of work canbe expressed in terms of one or more characteristics. In the computerprogramming field these characteristics can include a type ofprogramming language (e.g., JAVA, C, etc.) and a type of task (e.g.,coding, debugging, design, integration testing, etc.). Any knownstatistical software package that correlates multiple data inputs (e.g.,stress levels and tasks) in order to determine trends can be utilized byembodiments. Examples of statistical software packages include, but arenot limited to the open source statistical package R and Minitab fromMinitab Incorporated.

The statistical software package can be utilized to determine the leveland type of stress (e.g., positive stress, negative stress) on the userassociated with particular types of tasks. As used herein, the term“stress” refers to changes in a person's emotions due to externalstimuli. These changes can be positive or negative. The term “positivestress” refers to stress that indicates that a person feels better abouta task, which can lead to increased productivity. In contrast, the term“negative stress” refers to changes in a person that lead to decreasedproductivity. Positive stress and negative stress can be measuredagainst a baseline stress level that is measured when a person is innon-stress/focus situations (e.g., a resting heart rate or relaxedphased pupil dilation).

In an embodiment, the statistical package graphs the biometric feedbackdata (e.g., the biometric measurements) and the productivity data (e.g.,time spent and percentage of task completed, and/or expected completiondate and actual completion date) for tasks performed by the user. Forexample, if a task takes the user longer to complete than expected(and/or the user completes the task late) and the task is associatedwith a high level of stress, then in an embodiment the statisticalpackage can determine that the stress is negative stress indicating thatthe user did not enjoy performing the task. Conversely, if a task isperformed on time and/or in less time than expected and the task isassociated with a high level of stress, then in an embodiment thestatistical package can determine that the stress is positive stressindicating that the user enjoyed performing the task.

In embodiment, the output from the statistical package (e.g., acorrelation between a task and a negative or positive stress level) isused to generate correlated data for the user. The correlated datamatches characteristics or categories of the tasks to negative andpositive stress levels. For example, based on characteristics associatedwith the tasks, it can be determined that a particular user likes tasksthat involve interacting with other employees and does not like tasksthat have little social interaction. In another example, it can bedetermined that the user likes tasks having to do with debuggingcomputer code but does not like tasks having to do with writing newcomputer code. Additional levels, besides like and dislike, can beimplemented by embodiments. For example, there could be three or morelevels representing a scale from like to neutral to dislike.

In addition, the user can also enter input to indicate that there areother factors impacting the user's stress level and/or productivity on aparticular day (or other time span). These other factors may havenothing to do with the assigned tasks and instead be related to externalstimuli. External stimuli can include, but are not limited to the user'shealth, fatigue, and/or family issues. These other external factors canbe taken into account by the statistical analysis and/or when generatingthe correlated data. In an embodiment, the external factors registeredby the user allows for accounting of statistical anomalies, such as alarger value of standard deviation from the mean level of stress.

Referring back to FIG. 1, at block 106, additional, or new, tasks areassigned to the user based on the correlated data. At block 108, thebiometric feedback of the user is monitored while performing theadditional tasks and the correlated data is updated based on themonitoring. The generation of the correlated data can take into accounta learning curve when a user has been assigned to a new project or to anew type of task (or a task that the user has not performed in a while).In one embodiment this is performed by waiting for a specified number oftimes that the user performs a task having specified characteristicsbefore generating the correlated data for that category of task. Inaddition, embodiments can be utilized to determine how long it takes aparticular user to learn new skills. See FIG. 3 and the accompanyingdescription below for a more detailed description of an embodiment of aprocess for performing the task assigning of block 106 and themonitoring of block 108.

Referring now to FIG. 2, a process flow 200 for a data collection phasethat includes establishing a biometric baseline is generally shown inaccordance with an embodiment. In an embodiment, the processing shown inFIG. 2 is performed by a task management computer application executingon one or more processors. At block 202, a user wears a biometricsensor, such as a Q-sensor, to record biometric measurements. Inaddition, the user can register external stress inducers such as health,fatigue, and/or family issues. As known in the art, a Q-sensor can beworn as a bracelet and used to measure electro-dermal activity throughthe use of skin resistance.

When a Q-sensor is used to collect biometric measurements, the term“tonic skin conductance” or “skin conductance level (SCL)” refers to thelevel of skin conductance when the person is not subject to any knowndiscrete environmental event or external stimuli. Tonic skin conductancelevels typically vary slowly over time (e.g., over tens of seconds tominutes) based on a person's psychological state, hydration, skindryness, and autonomic regulation. “Phasic skin conductance” or “skinconductance response (SCR)” can be associated with short-term events andoccur in the presence of discrete environmental stimuli (e.g., sight,sound, smell, cognitive processes that precede an event such asanticipation, decision making, etc.). Phasic changes usually show up asabrupt increases in the skin conductance or peaks in the skinconductance. For example, if a software developer enjoys coding in Javaand the project he is working on primarily uses that language then he ismore likely to be happy with his work. These stress levels would then bemeasured by SCL. However if the same person is coding in C++, which heis new to, and has a presentation due soon this biometric feedback wouldbe characterized by SCR.

Any type of biometric sensor that can record biometric measurements of auser can be implemented by embodiments. Other sensors that can be worn,or attached, to the user include, but are not limited to heart ratemonitors and Fitbits. Additional biometric sensors that are not attachedto the user can also be utilized by embodiments. These sensors can becontained in a computer device utilized by the user and include, but arenot limited to pupil dilation detectors. In embodiments a singlebiometric sensor is utilized. In other embodiments, biometricmeasurements from two or more biometric sensors are combined todetermine a user's stress level or other biometric measurement.

In an embodiment, all or portions of block 204 are performedsimultaneously (or overlapping in time) with block 202. At block 204,the task management application is used by a manager or by the user toassign tasks (or projects made up of several tasks) to the user withclearly defined checkpoints and time frames. By completing these tasksor by reaching these checkpoints within the pre-decided time frames, theuser's level of productivity can be measured through a percentage ofwork complete measurement. The user or manager can enter thetask/checkpoint completion data into the task management application. Inan embodiment the task management application interfaces to RationalTeam Concert from IBM, however interfaces to other project managementsoftware applications can also be implemented by embodiments. At block206, the process of blocks 202 and 204 is performed in a loop for aspecified time period or until a specified number of tasks orcheckpoints are completed. In an embodiment, the specified number oftasks may include a specified number of tasks having particularcharacteristics.

Referring now to FIG. 3, a process flow 300 for assigning a new task andtracking biometric data of the user is generally shown in accordancewith an embodiment. In an embodiment, the processing shown in FIG. 3 isperformed by a task management application executing on one or moreprocessors. At block 302, a request to assign a new task to a user isreceived. The request can be received, e.g., from the user or from aproject manager. At block 304, correlated data associated with the useris accessed. In an embodiment, the correlated data correlatescharacteristics of tasks previously performed by the user with observedproductivity and stress levels of the user while performing the tasks.Each observed stress level is either a negative stress level or apositive stress level.

At block 306, a task that maximizes the productivity of the user whilereducing negative stress levels is identified. This can be performed byapplying an optimization model to select, from a plurality of potentialtasks, a task that maximizes a predicted productivity of the user inperforming the task while at the same time minimizes a predictednegative stress level of the user while performing the task. Input tothe optimization model can include the correlated data andcharacteristics of the plurality of potential tasks. The plurality ofpotential tasks can be a list of tasks in a project plan that have notyet been assigned. In am embodiment, this optimization model has twomajor parts. The first is an objective function, which is generally amathematical equation whose purpose is to identify the task that wouldmaximize the user's productivity. This maximization function isconstrained by the second part of the model, which includes a list ofmathematical constraints. These constraints use inequalities to restrictthe level of negative stress that the user is allowed to experience.These restrictions can be self imposed by the user based on factors likehealth and happiness. These two parts can then be fed into a programlike Express Project or R to identify the best task for the user fromthe list of available tasks.

In an embodiment, the optimization is performed across a project withthe correlated data of several users being taken into account. In thiscase the optimization model can sort through two lists. One listcontaining the users available to perform tasks and the other listcontains a list of tasks. The optimization model could be applied toeach combination of user and task based on a “bubble” sort technique.

In an embodiment, the selected task can be replaced (e.g., by a projectmanager) with a different task. A project manager can view all of thecorrelation data for the user, or the user and all of the team memberscombined. The system can suggest tasks for the user and/or other teammembers, and the project manager can agree to or override thesuggestion(s). The project manager can select a different task than thatsuggested by the system for the user and cause the system to assign thatdifferent task (instead of the suggested task). In other embodiments,the task selection and assignment is completely automated and the systemselects and assigns tasks to a user or group of users with the goal ofmaximizing productivity and/or lowering negative stress levels. Ifurther embodiments, a hybrid system that includes a combination ofmanual task selection and automated task selection is implemented.

At block 308, the task is assigned to the user. In an embodiment, theassignment is performed by a task management application based on theoptimization model output. Block 310 is performed to monitor the userwhile he or she is performed the task. In an embodiment, the monitoringproduces data related to an observed productivity of the user inperforming the selected task and an observed stress level of the userwhile performing the selected task (obtained, e.g., from a biometricsensor).

At block 312, the user completes the task. The correlated data for theuser can then be updated based on the observed productivity of the userin performing the selected task, the observed stress level of the userwhile performing the selected task, and at least one characteristic ofthe selected task. In an embodiment, the observed productivity is basedon one or more of: a comparison of an expected completion date and anactual completion date; and a comparison of an expected number of timeunits to complete the selected task and an actual number of time unitsto complete the selected task. The updating of the correlated data caninclude: based on the observed productivity, characterizing the observedstress level as a negative stress level or a positive stress level;associating the observed stress level and observed productivity with atleast one characteristic of the selected task; and storing results ofthe associating as correlated data for the user. The updating can alsotake into a previous stress level and productivity associated with theselected task for example by storing an average or weighted average (orsome other calculation) of the observed data and the previous correlateddata. In addition, external factors can also be taken into account whenupdating the correlated data.

Referring to FIG. 4, a block diagram of a system 400 for assigning tasksbased on biometric data is generally shown in accordance with anembodiment. The system 400 includes a task management application 410that is executed by one or more computer programs located on a hostsystem 404 and/or a user system(s) 402.

The system 400 depicted in FIG. 4 includes one or more user systems 402through which users at one or more geographic locations may contact thehost system 404 to initiate programs. The user systems 402 are coupledto the host system 404 via a network 406. Each user system 402 may beimplemented using a general-purpose computer executing a computerprogram for carrying out the processes described herein. The usersystems 402 may be user devices such as personal computers (e.g., a laptop, a tablet computer, a cellular telephone) or host attachedterminals. If the user systems 402 are personal computers, theprocessing described herein may be shared by a user system 402 and thehost system 404. The user systems 402 may also include game consoles,network management devices, and field programmable gate arrays. Inaddition, multiple user systems 402 and/or host systems 404 may beconcurrently operating to perform the processing described herein.

The network 406 may be any type of known network including, but notlimited to, a wide area network (WAN), a local area network (LAN), aglobal network (e.g. Internet), a virtual private network (VPN), a cloudnetwork, and an intranet. The network 406 may be implemented using awireless network or any kind of physical network implementation known inthe art. A user system 402 may be coupled to the host system throughmultiple networks (e.g., cellular and Internet) so that not all usersystems 402 are coupled to the host system 404 through the same network.One or more of the user systems 402 and the host system 404 may beconnected to the network 406 in a wireless fashion. In one embodiment,the network is the Internet and one or more user systems 402 execute auser interface application (e.g. a web browser) to contact the hostsystem 404 through the network 406. In another exemplary embodiment, theuser system 402 is connected directly (i.e., not through the network406) to the host system 404. In a further embodiment, the host system404 is connected directly to or contains one or more of the storagedevice 408.

The storage devices 408 include data relating to the tasks managementapplication 410 and may be implemented using a variety of devices forstoring electronic information. In an embodiment, data stored in thestorage devices 408 includes, but is not limited to, task managementdata (e.g., tasks to be assigned, assigned tasks and responsible user,target due dates and percent complete data, etc.), biometric data (datagenerated by a sensor), and correlated data, and other data utilized byembodiments described herein. It is understood that the storage devices408 may be implemented using memory contained in the host system 404 orthat they may be separate physical devices. The storage devices 408 maybe logically addressable as a consolidated data source across adistributed environment that includes the network 406. Informationstored in the storage devices 408 may be retrieved and manipulated viathe host system 404 and/or via a user system 402. Though shown as threeseparate storage devices 408 each holding different types of data, anynumber of other arrangements are also possible. Other non-limitingexamples follow. For example, a single storage device 408 can containall of the data pertaining to users and tasks on a particular project.In another example, a single storage device 408 can hold all of the datapertaining to one user. In another example, biometric data andcorrelated data for a user is stored on a storage device 408 located inthe user system 402 and the task management data is stored on a storagedevice 408 located on the host system 404.

The host system 404 depicted in FIG. 4 may be implemented using one ormore servers operating in response to a computer program stored in astorage medium accessible by the server. The host system 404 may operateas a network server (e.g., a web server) to communicate with the usersystem 402. The host system 404 handles sending and receivinginformation to and from the user system 402 and can perform associatedtasks. The host system 404 may also include a firewall to preventunauthorized access to the host system 404 and enforce any limitationson authorized access. For instance, an administrator may have access tothe entire system and have authority to modify portions of the system. Afirewall may be implemented using conventional hardware and/or softwareas is known in the art.

The host system 404 may also operate as an application server. The hostsystem 404 executes one or more computer programs, including a taskmanagement application 410, to provide aspects of embodiments asdescribed herein. Processing may be shared by the user system 402 andthe host system 404 by providing an application to the user system 402.Alternatively, the user system 402 can include a stand-alone softwareapplication for performing a portion or all of the processing describedherein. As previously described, it is understood that separate serversmay be utilized to implement the network server functions and theapplication server functions. Alternatively, the network server, thefirewall, and the application server may be implemented by a singleserver executing computer programs to perform the requisite functions.

Also shown in FIG. 4 is a biometric sensor 414 attached to or worn by auser 416. In an alternate embodiment, the biometric sensor 414 iscontained in the user system 402.

Technical effects and benefits include the ability to assign tasks basedon biometric feedback about a user.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the invention. Asused herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising,” when used in this specification, specify thepresence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition of oneor more other features, integers, steps, operations, elements,components, and/or groups thereof.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements in the claims below are intended toinclude any structure, material, or act for performing the function incombination with other claimed elements as specifically claimed. Thedescription of the present invention has been presented for purposes ofillustration and description, but is not intended to be exhaustive orlimited to the invention in the form disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the artwithout departing from the scope and spirit of the invention. Theembodiments were chosen and described in order to best explain theprinciples of the invention and the practical application, and to enableothers of ordinary skill in the art to understand the invention forvarious embodiments with various modifications as are suited to theparticular use contemplated.

The present invention may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein.

What is claimed:
 1. A method comprising: receiving, by a processor, arequest to assign a plurality of tasks of a project to a group of userslocated in a plurality of geographic locations; accessing, by theprocessor, correlated data that correlates characteristics of taskspreviously performed by each user in the group of users with theirobserved productivity and stress levels while performing the previoustasks, each observed stress level either a negative stress level or apositive stress level; applying, by the processor, an optimization modelto assign the plurality of tasks of the project to users in the group ofusers in a manner that maximizes a predicted productivity across theproject while at the same time minimizes a predicted negative stresslevel across the project, wherein input to the optimization modelincludes the correlated data and characteristics of the plurality oftasks; assigning the plurality of tasks of the project to users in thegroup of users based at least in part on output from the optimizationmodel; receiving, by the processor, an observed productivity of a userin the group of users while the user is performing one of the pluralityof tasks; collecting, by a biometric sensor, biometric data for the userin the group of users while the user is performing the one of theplurality of tasks, wherein the receiving the observed productivity ofthe user and the collecting biometric data for the user overlap in time;calculating an observed stress level of the user while performing theone of the plurality of tasks based at least in part on the biometricdata; and updating, by the processor, the correlated data for the userbased on the observed productivity of the user in performing the one ofthe plurality of tasks, the observed stress level of the user whileperforming the one of the plurality of tasks, and at least onecharacteristic of the one of the plurality of tasks.
 2. The method ofclaim 1, wherein the observed productivity is based on one or more of acomparison of an expected completion date and an actual completion date,and a comparison of an expected number of time units to complete theselected task and an actual number of time units to complete theselected task, and the updating the correlated data comprises: based onthe observed productivity, characterizing the observed stress level as anegative stress level or a positive stress level; associating theobserved stress level and observed productivity with the at least onecharacteristic of the selected task; and storing results of theassociating.
 3. The method of claim 2, wherein the associating takesinto account a previous stress level and productivity associated withthe selected task.
 4. The method of claim 1, wherein the biometricsensor includes a Q-sensor that is attached to the user.
 5. The methodof claim 1, wherein the biometric sensor includes a heart-rate monitorthat is attached to the user.
 6. The method of claim 1, wherein thebiometric sensor includes a pupil dilation detector.
 7. The method ofclaim 1, wherein the biometric sensor includes two or more of aQ-sensor, a heart-rate monitor, and a pupil dilation detector.
 8. Themethod of claim 1, wherein the updating the correlated data is furtherbased on external factors about the user.
 9. The method of claim 1,further comprising determining whether the user in the group of usershas elected to allow collection of the biometric data for the user inthe group of users, and wherein the collecting, calculating, andupdating are performed based at least in part on determining that theuser has elected to allow collection of the biometric data.